Sam K Vanspauwen, Virginia Luque-Fernández, Hanne B Rasmussen
{"title":"一个基于相关性的定量膜周期骨架相关周期的工具。","authors":"Sam K Vanspauwen, Virginia Luque-Fernández, Hanne B Rasmussen","doi":"10.3389/fninf.2025.1628538","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>The advent of super-resolution microscopy revealed the membrane-associated periodic skeleton (MPS), a specialized neuronal cytoskeletal structure composed of actin rings spaced 190 nm apart by two spectrin dimers. While numerous ion channels, cell adhesion molecules, and signaling proteins have been shown to associate with the MPS, tools for accurate and unbiased quantification of their periodic localization remain scarce.</p><p><strong>Methods: </strong>We developed Napari-WaveBreaker (https://github.com/SamKVs/napari-k2-WaveBreaker), an open-source plugin for the Napari image viewer. The tool quantifies MPS periodicity using autocorrelation and assesses periodic co-distribution between targets using cross-correlation. Performance was evaluated using both simulated datasets and STED microscopy images of periodic and non-periodic axonal proteins.</p><p><strong>Results: </strong>Napari-WaveBreaker output parameters accurately reflected the visually observed periodicity and detected spatial shifts between two periodic targets. The approach was robust across varying image qualities and reliably distinguished periodic from non-periodic protein distributions.</p><p><strong>Discussion: </strong>Napari-WaveBreaker provides an unbiased, quantitative framework for analyzing MPS-associated periodicity and co-distribution enabling new insights into the molecular organization and modulation of the MPS.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1628538"},"PeriodicalIF":2.5000,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411523/pdf/","citationCount":"0","resultStr":"{\"title\":\"A correlation-based tool for quantifying membrane periodic skeleton associated periodicity.\",\"authors\":\"Sam K Vanspauwen, Virginia Luque-Fernández, Hanne B Rasmussen\",\"doi\":\"10.3389/fninf.2025.1628538\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>The advent of super-resolution microscopy revealed the membrane-associated periodic skeleton (MPS), a specialized neuronal cytoskeletal structure composed of actin rings spaced 190 nm apart by two spectrin dimers. While numerous ion channels, cell adhesion molecules, and signaling proteins have been shown to associate with the MPS, tools for accurate and unbiased quantification of their periodic localization remain scarce.</p><p><strong>Methods: </strong>We developed Napari-WaveBreaker (https://github.com/SamKVs/napari-k2-WaveBreaker), an open-source plugin for the Napari image viewer. The tool quantifies MPS periodicity using autocorrelation and assesses periodic co-distribution between targets using cross-correlation. Performance was evaluated using both simulated datasets and STED microscopy images of periodic and non-periodic axonal proteins.</p><p><strong>Results: </strong>Napari-WaveBreaker output parameters accurately reflected the visually observed periodicity and detected spatial shifts between two periodic targets. The approach was robust across varying image qualities and reliably distinguished periodic from non-periodic protein distributions.</p><p><strong>Discussion: </strong>Napari-WaveBreaker provides an unbiased, quantitative framework for analyzing MPS-associated periodicity and co-distribution enabling new insights into the molecular organization and modulation of the MPS.</p>\",\"PeriodicalId\":12462,\"journal\":{\"name\":\"Frontiers in Neuroinformatics\",\"volume\":\"19 \",\"pages\":\"1628538\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411523/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Neuroinformatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fninf.2025.1628538\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Neuroinformatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fninf.2025.1628538","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
A correlation-based tool for quantifying membrane periodic skeleton associated periodicity.
Introduction: The advent of super-resolution microscopy revealed the membrane-associated periodic skeleton (MPS), a specialized neuronal cytoskeletal structure composed of actin rings spaced 190 nm apart by two spectrin dimers. While numerous ion channels, cell adhesion molecules, and signaling proteins have been shown to associate with the MPS, tools for accurate and unbiased quantification of their periodic localization remain scarce.
Methods: We developed Napari-WaveBreaker (https://github.com/SamKVs/napari-k2-WaveBreaker), an open-source plugin for the Napari image viewer. The tool quantifies MPS periodicity using autocorrelation and assesses periodic co-distribution between targets using cross-correlation. Performance was evaluated using both simulated datasets and STED microscopy images of periodic and non-periodic axonal proteins.
Results: Napari-WaveBreaker output parameters accurately reflected the visually observed periodicity and detected spatial shifts between two periodic targets. The approach was robust across varying image qualities and reliably distinguished periodic from non-periodic protein distributions.
Discussion: Napari-WaveBreaker provides an unbiased, quantitative framework for analyzing MPS-associated periodicity and co-distribution enabling new insights into the molecular organization and modulation of the MPS.
期刊介绍:
Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide.
Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states.
Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.